Big Data: Characteristics, Challenges, Techniques and Business Support

This report discusses the characteristics, challenges, techniques and business support of Big Data. It explains the importance of Big Data in helping businesses make better decisions, improve product quality, and tap potential customers. The challenges of Big Data analytics are also discussed, including the lack of skilled professionals, data growth, confusion in tool selection, data safety, and lack of understanding. The techniques currently available to analyze Big Data are also presented, including A/B testing, data fusion and integration, statistics, machine learning, natural language processing, and data mining. The report also includes a poster on Big Data.

BSc (Hons) Business Management
BMP4005
Information Systems and Big Data
Analysis
Poster and Summary Paper
Submitted by:
Name:
ID:
1

Contents
Introduction 3
What big data is and the characteristics of big data 3
The challenges of big data analytics 3-4
The techniques that are currently available to analyse big data
4
How Big Data technology could support business, an explanation
with examples 5
References 5-6
Appendix 1: Poster 6
2

Introduction
Big data refers to the combination of an organized and unorganized or semi-
organized form of information gathered by an origination which is used in various
type of projects such as machine acquisition projects, predictive modeling etc. It
helps a firm to handle a large quantity of data. Collected data is an unorganized data
which have to be organized by the company in tabular form. This report includes
various important thoughts regarding big data, such as multiple types of challenges
faced by organization while collecting and organizing big data, and important
techniques are discussed present in the market. Big Data ensures that the analysis
is done on the data synchronized.
What big data is and the characteristics of big data?
Big data includes vast information, sets of complex data, mainly collected from new or
fresh sources. These data have a mixture of various variety information; it has huge
volume and velocity. This report considers three V's that refer to Variety, velocity and
volume. It provides strength to the companies to make profitable decisions. Here is some
example of industries that use big data technologies and they are medical, agriculture
etc. The main characteristics of big data are discussed below:
1. Volume: Big data is always in vast quantity and it is collected in raw form,
which cannot be used directly. Raw data is converted in to the structured manner with
the help of tables, charts etc. Data is collected from various sources which include
unstructured figures and facts. Data is collected for a particular aim or objectives of a
company.
2. Variety: It refers to the data which is recorded by a machine or individual in
semi-organized or unorganized form for a specific purpose. Structured data includes
texts, picture and videos. On the other handhand, unstructured data includes voice-mails,
hand written text or recordings. Variety refers to to categories of various types of data in
an efficient manner.
3. Velocity: It refers to the rate at which information is collected, summarized of
the varied data.
4. value: It is an essential aspect of big data. It ensureensures that the
collection of data is matching with the objectives set by the company. Big data is
helpful in discovering the information patterns which support the growth of the company.
The challenges of big data analytics
Technology plays an important role in the day to day operations and activities.
Here are some major challenges discussed below that is faced by big data analytic:
1. Lack of skilled professionals: An Organization needs skilled and
professional individuals to use such modern techniques. Companies require Data
analysts, scientists and other data technologists to useduse these tools and make use of
large data sets. This is a major problem which is faced by the institutions because of
continuous change in technologies and improvements. In most of situations, an individual
is not able to take step to fulfill this gap of knowledge. Hiring professionals may increase
the cost of company projects.
3

2. Problem of data growth: One of the major challenges of big data analytic
is to store the data available for the study. Increase in the quantity of data set laid
down the various uses of understanding being data stock is increasing continuously.
These information sets are growing by passing time, it is hard to handle information.
Most of the data is in unstructured or semi-structured form that comes from various
documents, recordings etc. and is converted into information which is used for
analysis.
3. Confusion in tool selection: Companies might be confused in selection of
easy tools to analyze huge data sets. It is very important to opt suitable and easiest
method which give more accurate data from these data sources. If data is not correct
then time, efforts and working hour are wasted.
4. Data safety: It is very difficult task to provide security to the data sets
because they are very large. Institutions are busy in analyzing, storing and
summarizing their information that they forget to provide security for confidential
information. This is not a good move as unsecured information secretary can
become fruitful path for the hackers.
5. lack of understanding: Institutions facing downfalls in their big data efforts
because of lack of understanding (Singh and et.al., 2021). Not all employee know
about big data, its storage, importance and procedures. Company has to appoint
data professionals to understand big data techniques. It may lead to conflicts or
failures because of improper understanding.
The techniques that are currently available to analyse big
data
McKinsey's identifies the various techniques of big data in the report that are
taken form different sources such as mathematics, statistics etc. some are discussed
below:
1. A/B testing: A/B testing involves relation between a control group with a
various form of test group, in order to identify what modification will enhance a giver
objective. It creates a hypothesis of variables which include dependent or
independent variable (van Leeuwen, 2019). Big data suitable in this model also, it
can trial vast numbers, however it is only helpful when the collected data is
meaningful.
2. Data fusion and integration: It refers to the combination of various
techniques which analyses data from aggregate sources and solution. It is a
procedure that analyses the information to reach on conclusions and make
recommendations for the same.
3. Statistics: This is a very common method used in big data analytics that
includes accumulation, organization, interpretation and summarize data. Statics is
mainly separated into two categories such as descriptive statistics and inferential
statistics. One is work with the measures of central tendency and another help to test
the information.
4. Machine learning: It is a modern form to analyze data. This is most
common in the field of AI and it is widely use in data analysis. It works with the help
of computer rule to prepare prediction based on data (Verma, Mankad and Garg,
2018). It provides assumption that are not possible to predict by humans.
5. Natural language processing: It is also known as an internal medicine of
computer science, artificial intelligence and scientific discipline, this data method is
4

using algorithms to understand human language. It can easily find out the bugs and
errors and also remove them.
6. Data mining: It exclude the pattern from the vast data sets by joining
techniques from statics and machine learning, within the database management. In
other words, it extracts the important information related to specific product or
services that can provide better understanding about them.
How Big Data technology could support business, an
explanation with examples
Big data plays vital role in the businesses to measure the changing trends that take
place in the current market environment and it also analyze the consumer behavior,
needs and demands of them. The various kinds that help to analyze the support of
technologies in the businesses are discussed below:
 Enhance the product quality: Big data help in analyzing the demand of the consumers
which help to improve the quality of existing product. It also suggests the company to
launch new product to meet consumer wants. Big data provide support in that areas
where company is lacking.
 Data safety and security: Big data ensure that the data is stored at a place where it is
secured and protected from the various risks, frauds and hacking. Data is kept under
high technologies which help company to run without any errors (Wahdain,
Baharudin and Ahmad, 2018).
 Better decision making: Data techniques help in improving business decision making
process in order to maximize its profits. It provides huge data about competitors and
their product that help to compare our product with them. This will help company to
prepare future strategies in order to improve quality and standard of the product.
 Optimum use of scarce resources: Big data provide knowledge about the resources
which are limited in the world and it would also help business to minimize the
wastage of resources. It will also decrease the cost of production and provide
suggestions related to best utilization of the resources. It also increases the revenue of
the firm (Willetts, Atkins and Stanier, 2020).
 Tap potential customers: Big data includes the data about consumer’s behavior,
attitude and trends that will help firm in their future strategies. It includes various
tools that connect with the people or consumers to analyze their needs which would
help in caring out business activities smoothly. It also helps in expending business in
that areas which are not covered from past years and it also help to improve the
profitability of the businesses.
References
Kumar, P.A.V., 2018. The use of big data analytics in information systems
research. Available at SSRN 3185883.
Pani, S.K and et.al., 2021. Applications of Machine Learning in Big-Data Analytics
and Cloud Computing (pp. i-xxxii). River Publishers.
Rachman, Z.A., 2019, July. Big data analytics in airlines: Efficiency evaluation using
DEA. In 2019 7th International Conference on Information and
Communication Technology (ICoICT) (pp. 1-6). IEEE.
5

Singh, H and et.al., 2021, January. Understanding Brand Authenticity Sentiments
using Big Data Analytics. In 2021 11th International Conference on Cloud
Computing, Data Science & Engineering (Confluence) (pp. 304-308). IEEE.
van Leeuwen, L., 2019. High-Throughput Big Data Analytics Through Accelerated
Parquet to Arrow Conversion.
Verma, J.P., Mankad, S.H. and Garg, S., 2018, December. Big data analytics:
performance evaluation for high availability and fault tolerance using
mapreduce framework with hdfs. In 2018 fifth international conference on
parallel, distributed and grid computing (PDGC) (pp. 770-775). IEEE.
Wahdain, E.A., Baharudin, A.S. and Ahmad, M.N., 2018, June. Big data analytics in
the Malaysian public sector: the determinants of value creation.
In International Conference of Reliable Information and Communication
Technology (pp. 139-150). Springer, Cham.
Willetts, M., Atkins, A.S. and Stanier, C., 2020, October. Barriers to SMEs adoption
of big data analytics for competitive advantage. In 2020 Fourth International
Conference On Intelligent Computing in Data Sciences (ICDS) (pp. 1-8).
IEEE.
Appendix 1: Poster
6